Early System Failure Detection through System Log Analysis: An LSTM Approach

In today’s digital age, the creation of an intelligent forecasting system within digital devices is essential. Predictive maintenance for digital devices leverages data analytics and monitoring technologies to predict and prevent device malfunctions proactively. This study focuses on utilizing log data to analyze patterns within log files, incorporating various crucial parameters like application performance, security events, and memory usage, each timestamped to identify potential failure triggers. Key parameters include Word2Vec embeddings from the gensim library, enriching our understanding of the semantic relationships between Log Messages and Log Levels. This enhanced dataset is then input into a Long-Short Term Model (LSTM) trained on contextual embeddings to establish meaningful connections between log messages and levels. By employing advanced data extraction techniques and deriving valuable insights from log data, the aim is to construct a predictive model capable of anticipating issues such as unexpected failures, improving performance, and addressing security vulnerabilities. The methodology involves a systematic approach encompassing data collection, pre-processing, and the application of natural language processing to handle textual data. Various methods, including log data collection, pre-processing, and the development of a deep learning-based prediction model, are executed to diagnose system failures effectively. Validation of the model is conducted through experiments on a test bed comprising systems with diverse configurations. The ultimate objective is to enhance user experience by ensuring a smooth and secure computing environment through proactive issue anticipation and prevention. A test bed is established for experimentation, connecting systems with different configurations to capture and analyze real-time log data for system health assessment. Deep learning techniques based on the LSTM model are employed to analyze sequential logs efficiently.